HINT proposes a continual learning framework using interval arithmetic in embedding space with a hypernetwork to generate target network weights, achieving improved scalability and non-forgetting guarantees over InterContiNet while outperforming several benchmarks, though struggling with complex datasets.
Continual Learning, Representation Learning, Embeddings, Efficiency
Patryk Krukowski, Anna Bielawska, Kamil Książek, Paweł Wawrzyński, Paweł Batorski, Przemysław Spurek
IDEAS NCBR, Jagiellonian University, Heinrich Heine Universität Düsseldorf, IDEAS Institute
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Background Problem
Continual Learning (CL) addresses the challenge of learning new tasks sequentially without forgetting previously learned ones, a problem known as catastrophic forgetting. Existing methods often lack solid guarantees against forgetting, especially in complex scenarios. The paper builds on Interval Continual Learning (InterContiNet), which uses interval arithmetic to constrain neural network weights for non-forgetting guarantees, but struggles with high-dimensional weight spaces and scalability to large datasets. HINT aims to solve these issues by shifting interval arithmetic to a lower-dimensional embedding space and using a hypernetwork to map these to target network weights, improving training efficiency and scalability while maintaining non-forgetting guarantees.
Method
HINT (Hypernetwork Interval Training) introduces a novel CL architecture where interval arithmetic is applied in a low-dimensional embedding space rather than the high-dimensional weight space. Task-specific interval embeddings are trained and fed into a hypernetwork, which transforms them into interval weights for a target network using Interval Bound Propagation (IBP). The training process ensures performance preservation for previous tasks via regularization of the hypernetwork outputs. Key steps include: (1) defining interval embeddings for each task with controlled perturbation to ensure non-empty intersections, (2) propagating these intervals through the hypernetwork to generate target network weights, and (3) optionally creating a universal embedding from interval intersections to produce a single set of weights for all tasks, eliminating the need for storing multiple embeddings or the hypernetwork during inference. This approach reduces complexity and provides theoretical non-forgetting guarantees if regularization is effective and embedding intersections are non-empty.
Experiment
Experiments were conducted on datasets like Permuted MNIST, Split MNIST, Split CIFAR-10, Split CIFAR-100, and TinyImageNet under Task-Incremental Learning (TIL), Domain-Incremental Learning (DIL), and Class-Incremental Learning (CIL) setups. HINT outperforms InterContiNet across all tested scenarios, with significant improvements in TIL (e.g., 79.23% vs. 42.0% on Split CIFAR-100). Compared to other state-of-the-art methods, HINT achieves top results in several TIL benchmarks (e.g., 97.78% on Permuted MNIST) but shows mixed performance in CIL, with high variance in results (e.g., Split MNIST). The setup is comprehensive, covering various architectures (MLPs, ResNet-18, AlexNet) and scenarios, though the universal embedding struggles with complex datasets like Split CIFAR-100 (only ~15% accuracy). The relaxation technique for convolutional networks aids training but compromises strict interval guarantees. Results generally match expectations for simpler tasks but reveal limitations in scalability and consistency for complex, multi-class tasks, suggesting the method’s effectiveness is context-dependent.
Further Thoughts
The concept of using a hypernetwork as a meta-trainer in HINT opens up interesting avenues for exploration in other areas of machine learning, such as transfer learning or meta-learning, where dynamic weight generation could adapt models to diverse tasks without retraining from scratch. However, the observed performance drop with complex datasets like Split CIFAR-100 suggests a potential connection to the capacity limits of universal embeddings, reminiscent of challenges in multi-task learning where shared representations fail to capture task-specific nuances. Future work could explore hybrid approaches combining HINT’s interval-based constraints with generative replay methods to enhance robustness for larger task sets. Additionally, investigating the impact of different hypernetwork architectures or regularization strategies could address the scalability issues seen in Permuted MNIST-100, potentially linking to broader research on neural architecture search for adaptive systems. This intersection of interval arithmetic and hypernetworks also raises questions about applicability to privacy-preserving learning, where constrained weight spaces might limit information leakage, warranting further interdisciplinary study.